import sys
sys.path.append("..")
sys.path.append("../..")
from RumdetecFramework.BaseRumorFramework import RumorDetection
from Dataloader.twitterloader import TwitterSet
from Dataloader.dataloader_utils import Sort_data, Lemma_Factory, shuffle_data
from SentModel.Sent2Vec import *
from PropModel.SeqPropagation import *
from sklearn.feature_extraction.text import TfidfVectorizer
import torch.nn as nn
import os
import pickle

if len(sys.argv) !=1:
    os.environ['CUDA_VISIBLE_DEVICES'] = sys.argv[1]

def obtain_LSTMRD(pretrained_vectorizer):
    lvec = TFIDFBasedVec(pretrained_vectorizer, 20, embedding_size=300, w2v_dir="../../saved/glove_en/")
    prop = GRUModel(sent_hidden_size=300, prop_hidden_size=100, num_layers=2, dropout_prob=0.2)
#     prop = LSTMModel(sent_hidden_size=100, prop_hidden_size=100, num_layers=1, dropout_prob=0.2)
    cls = nn.Linear(100, 2)
    LSTMRD = RumorDetection(lvec, prop, cls, batch_size=20, grad_accum_cnt=1)
    return LSTMRD

def obtain_Sort_set(tr_prefix, dev_prefix, te_prefix):
    tr_set = TwitterSet()
    tr_set.load_data_fast(data_prefix=tr_prefix, min_len=5)
    dev_set = TwitterSet()
    dev_set.load_data_fast(data_prefix=dev_prefix)
    te_set = TwitterSet()
    te_set.load_data_fast(data_prefix=te_prefix)
    return tr_set, dev_set, te_set

log_dir = str(__file__).rstrip(".py")
if not os.path.exists(log_dir):
    os.system("mkdir %s"%log_dir)
else:
    os.system("rm -rf %s" % log_dir)
    os.system("mkdir %s" % log_dir)

Tf_Idf_twitter_file = "../../saved/TfIdf_twitter.pkl"
if os.path.exists(Tf_Idf_twitter_file):
    with open(Tf_Idf_twitter_file, "rb") as fr:
        tv = pickle.load(fr)
else:
    i = 1
    tr, dev, te = obtain_Sort_set("../../data/twitter_tr%d" % i,
                                  "../../data/twitter_dev%d" % i,
                                  "../../data/twitter_te%d" % i)
    lemma = Lemma_Factory()
    corpus = [" ".join(lemma(txt)) for data in [tr, dev, te] for ID in data.data_ID for txt in data.data[ID]['text']]
    tv = TfidfVectorizer(use_idf=True, smooth_idf=True, norm=None)
    _ = tv.fit_transform(corpus)

for t in range(10):
    i = 1
    tr, dev, te = obtain_Sort_set("../../data/twitter_tr%d" % i,
                                  "../../data/twitter_dev%d" % i,
                                  "../../data/twitter_te%d" % i)
    tr, dev, te = Sort_data(tr, dev, te)
    tr.filter_short_seq(min_len=5)
    tr.trim_long_seq(10)
    print("%s : (dev event)/(test event)/(train event) = %3d/%3d/%3d" % (te.data[te.data_ID[0]]['event'], len(dev), len(te), len(tr)))
    print("\n\n===========Sort Train===========\n\n")
    model = obtain_LSTMRD(tv)
    model.train_iters(tr, dev, te, max_epochs=50,
                    log_dir=log_dir, log_suffix="Sort",
                    model_file="../../saved/TFIDF_GRU2RD_Sort.pkl")
    # te_loader = DataLoader(te, batch_size=5, shuffle=False, collate_fn=te.collate_raw_batch)
    # rst = model.valid(te_loader, pretrained_file="../../saved/TFIDF_GRU2RD_Sort.pkl", all_metrics=True)
    # print("##Validation On Test Dataset####  te_acc:%3.4f, te_loss:%3.4f, te_prec:%3.4f, te_recall:%3.4f, te_f1:%3.4f"%rst)
    tr, dev, te = obtain_Sort_set("../../data/twitter_tr%d" % i,
                                  "../../data/twitter_dev%d" % i,
                                  "../../data/twitter_te%d" % i)
    tr, dev, te = shuffle_data(tr, dev, te)
    tr.filter_short_seq(min_len=5)
    tr.trim_long_seq(10)
    print("%s : (dev event)/(test event)/(train event) = %3d/%3d/%3d" % (te.data[te.data_ID[0]]['event'], len(dev), len(te), len(tr)))
    print("\n\n===========Sort Train===========\n\n")
    model = obtain_LSTMRD(tv)
    model.train_iters(tr, dev, te, max_epochs=50,
                    log_dir=log_dir, log_suffix="General",
                    model_file="../../saved/TFIDF_GRU2RD_Sort.pkl")

    for i in range(5):
        tr, dev, te = obtain_Sort_set("../../data/twitter_tr%d" % i, "../../data/twitter_dev%d" % i, "../../data/twitter_te%d" % i)
        tr.filter_short_seq(min_len=5)
        tr.trim_long_seq(10)
        test_event_name = te.data[te.data_ID[0]]['event']
        print("%s : (dev event)/(test event)/(train event) = %3d/%3d/%3d" % (
        test_event_name, len(dev), len(te), len(tr)))
        print("\n\n===========%s Train===========\n\n"%te.data[te.data_ID[0]]['event'])
        model = obtain_LSTMRD(tv)
        model.train_iters(tr, dev, te, max_epochs=50,
                          log_dir=log_dir, log_suffix=test_event_name,
                          model_file="../../saved/TFIDF_GRU2RD_%s.pkl" %test_event_name)

